GAI & Agentic AI Course

Master AWS Bedrock, RAG, AI Agents, and Agentic AI systems with hands-on practical training

Instructor: Noor Sabahi — AWS Ambassador, Senior AI & Cloud Engineer

Basic → Advanced 8 Sessions × 60 min Hands-on Labs Final Project

Course Overview

A comprehensive, hands-on journey through AWS Generative AI and Agentic AI technologies.

This 8-session practical course covers everything from AWS Bedrock fundamentals to building production-ready agentic AI systems. You'll master Large Language Models, embeddings, vector databases, Retrieval Augmented Generation (RAG), and advanced AI agents using AWS services.

Each session combines theory with hands-on labs, ensuring you gain practical experience with real-world applications. By the end of the course, you'll have built a complete project combining RAG, AI Agents, and the Model Context Protocol (MCP), deployed on AWS infrastructure.

Course Information
  • Duration: 8 sessions × 60 min
  • Level: Basic → Advanced
  • Instructor: AWS Ambassador
  • Format: Online / Live Sessions
  • Price: $450 (1 student) / $600 (2-4)
  • Certificate: Yes
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Course Curriculum

  • Difference between Traditional AI and Generative AI
  • Core LLM concepts: Prompting, Tokens, and Context Windows
  • Overview of AWS Bedrock
  • Hands-on: Experiment with Bedrock Playground using Claude and Nova LLMs

  • Comparison between popular LLMs (Claude, Titan, Llama, Mistral)
  • Using AWS SDK and Bedrock API for inference
  • Hands-on: Write a Python script to perform Prompt Engineering with an LLM

  • What are Embeddings and why they're needed
  • Converting text into numerical representations
  • Examples of Vector Databases: FAISS, OpenSearch
  • Introduction to Amazon Knowledge Base and its uses
  • Hands-on: Generate embeddings using Amazon Titan Embeddings

  • How FAISS stores and searches embeddings
  • Types of search: Similarity Search (KNN) and Exact Search
  • Hands-on: Build a vector database and perform similarity retrieval from an index

  • What is RAG and why it enhances LLM capabilities
  • Use cases for RAG
  • RAG Solution Architecture on AWS
  • Hands-on: Build a complete RAG-based application

  • Understanding AWS Bedrock Knowledge Base
  • How Bedrock automates data ingestion and retrieval
  • Hands-on: Connect Bedrock KB to a data source (S3 or document store) and run queries
  • Review of Knowledge Base solution architecture in AWS

Part 1: AI Agents
  • What is an AI Agent and how it differs from standard LLMs
  • Components of an Agent: Memory, Planning, Tools, Reasoning
  • Introduction to Amazon Bedrock Agents
  • Hands-on: Create and test an agent using Bedrock Console or SDK
Part 2: Model Context Protocol (MCP)
  • What is MCP and its role in Agentic AI ecosystems
  • How an MCP Server organizes data flow
  • Architecture of MCP Client/Server solutions
  • Hands-on: Build an example using MCP Client and Server

  • Difference between AI Agents and Agentic AI
  • Deep dive into AWS AgentCore architecture and components:
    • AgentCore Gateway
    • AgentCore Memory
    • AgentCore Identity
    • AgentCore Runtime
    • AgentCore Observability
  • Hands-on: Use case examples and building an AgentCore-based solution

What You'll Master

By completing this course, you will:

Master AWS Bedrock

Work confidently with Claude, Nova, Titan, and other LLMs via Bedrock

Build RAG Applications

Create production-ready Retrieval Augmented Generation systems

Develop AI Agents

Build intelligent agents with memory, planning, tools, and reasoning

Implement Agentic AI

Deploy AgentCore solutions and understand MCP architecture

Work with Vector Databases

Use FAISS, OpenSearch, and Bedrock Knowledge Bases effectively

Deploy to AWS

Production deployment using ECS, Lambda, and EC2

Final Project

Build a complete end-to-end project that combines:

  • Retrieval Augmented Generation (RAG)
  • AI Agents with autonomous capabilities
  • Model Context Protocol (MCP) integration
  • Deployment on AWS services (ECS, Lambda, or EC2)

Deliverable: Share your project and artifacts via GitHub repository

Certificate of Completion

Earn your digital certificate by:

  • ✅ Completing all hands-on sessions
  • ✅ Successfully delivering the final project

A digital certificate is issued after review and project submission.

Ready to Master AWS Generative & Agentic AI?

8 practical sessions with hands-on labs and a complete final project

$450

Individual Student

$600

Group (2-4 students)

Payment methods: PayPal, Stripe, PayTabs

Enroll Now